Environmental Hydraulics, Turbulence and Sediment Transport

Pu, Jaan H.

doi:10.3390/fluids7020048

Cited by 3 publications

(1 citation statement)

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“…The use of GIS with hydro-meteorological information is very important for development of any type of hydrological model [1], which in turns help in watershed study. The flow in the meandering nature of floodplain region is greatly differ from other flow conditions of the river and possess a significant effect on the hydrological model and flood modelling [2,3].…”

Section: Introductionmentioning

confidence: 99%

Assessing the Suitability of the SWAT Model for Hydro-Meteorological Analysis in the Lower Gangetic Floodplain Region of India

Pu,

Shivashankar,

Kundu

2024

Preprint

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In Tropical Monsoon climates, precipitation exceeds evapotranspiration, leading to increased surface runoff, especially in floodplain regions. The Bhagirathi-Hooghly River in India is the focus of the study, which analyses hydrology, sediment yield, and water balance in this floodplain area. This investigation employs the Soil and Water Assessment Tool (SWAT) model within the ArcGIS environment, using remote sensing data. The study area is divided into 4 sub-basins and 40 Hydrological Response Units (HRUs), with a focus on Land Use/Land Cover (LULC) parameters, soil attributes, and slope characteristics. Calibration (1998-2010) and validation (2011-2018) of river discharge and sediment yield are performed using the SUFI-2 algorithm within SWAT-CUP. The calibration process shows a strong correlation between observed and simulated data points, indicating the model's accuracy. The study reveals a pattern of early-year evapotranspiration followed by increasing rainfall during the monsoon season, leading to heightened surface runoff and sediment yield. Excessive rainfall, therefore, emerges as a pivotal driver of flood incidences within the floodplain. The insights derived from the SWAT model's floodplain data have the potential to inform flood control and sedimentation mitigation strategies. The assimilation of this data can empower local stakeholders and contribute to sustainable livelihoods and improved living conditions, offering guidance for future governmental policies.

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Section: Introductionmentioning

confidence: 99%